{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "from dowhy import CausalModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>bib6</th>\n",
       "      <th>bio6</th>\n",
       "      <th>bib8</th>\n",
       "      <th>bio8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10775</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10776</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10777</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10778</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10779</td>\n",
       "      <td>Friday</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    date    weekday  day  month  year  bib6  bio6  bib8  bio8\n",
       "0  10775     Monday    1      7  1929   141   141   169   169\n",
       "1  10776    Tuesday    2      7  1929   141   141   169   169\n",
       "2  10777  Wednesday    3      7  1929   141   141   169   169\n",
       "3  10778   Thursday    4      7  1929   141   141   169   169\n",
       "4  10779     Friday    5      7  1929   141   141   169   169"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\n",
    "    \"../data/banks.csv\"\n",
    ")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>bib6</th>\n",
       "      <th>bio6</th>\n",
       "      <th>bib8</th>\n",
       "      <th>bio8</th>\n",
       "      <th>lbib6</th>\n",
       "      <th>lbio6</th>\n",
       "      <th>lbib8</th>\n",
       "      <th>lbio8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1929-07-01</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>5.129899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1930-07-01</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1930</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>4.905275</td>\n",
       "      <td>4.905275</td>\n",
       "      <td>5.105945</td>\n",
       "      <td>5.105945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>1931-07-01</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1931</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>132</td>\n",
       "      <td>130</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>4.882802</td>\n",
       "      <td>4.867534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>1932-07-01</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1932</td>\n",
       "      <td>113</td>\n",
       "      <td>111</td>\n",
       "      <td>120</td>\n",
       "      <td>118</td>\n",
       "      <td>4.727388</td>\n",
       "      <td>4.709530</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>4.770685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>1933-07-01</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1933</td>\n",
       "      <td>102</td>\n",
       "      <td>100</td>\n",
       "      <td>111</td>\n",
       "      <td>110</td>\n",
       "      <td>4.624973</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>4.709530</td>\n",
       "      <td>4.700480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826</th>\n",
       "      <td>1934-07-01</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1934</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>109</td>\n",
       "      <td>108</td>\n",
       "      <td>4.624973</td>\n",
       "      <td>4.624973</td>\n",
       "      <td>4.691348</td>\n",
       "      <td>4.682131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    weekday  day  month  year  bib6  bio6  bib8  bio8  \\\n",
       "0    1929-07-01     Monday    1      7  1929   141   141   169   169   \n",
       "365  1930-07-01    Tuesday    1      7  1930   135   135   165   165   \n",
       "730  1931-07-01  Wednesday    1      7  1931   121   121   132   130   \n",
       "1096 1932-07-01     Friday    1      7  1932   113   111   120   118   \n",
       "1461 1933-07-01   Saturday    1      7  1933   102   100   111   110   \n",
       "1826 1934-07-01     Sunday    1      7  1934   102   102   109   108   \n",
       "\n",
       "         lbib6     lbio6     lbib8     lbio8  \n",
       "0     4.948760  4.948760  5.129899  5.129899  \n",
       "365   4.905275  4.905275  5.105945  5.105945  \n",
       "730   4.795791  4.795791  4.882802  4.867534  \n",
       "1096  4.727388  4.709530  4.787492  4.770685  \n",
       "1461  4.624973  4.605170  4.709530  4.700480  \n",
       "1826  4.624973  4.624973  4.691348  4.682131  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['date'] = data[['year', 'month', 'day']].apply(\n",
    "    lambda row: datetime.date(row.iloc[0], row.iloc[1], row.iloc[2]),\n",
    "    axis=1\n",
    ")\n",
    "\n",
    "data['date'] = pd.to_datetime(data['date'])\n",
    "\n",
    "for col in ['bib6', 'bio6', 'bib8', 'bio8']:\n",
    "    data[f'l{col}'] = data[col].apply(lambda x: np.log(x))\n",
    "\n",
    "data = data[(data['month']==7) & (data['day']==1)]\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>bib</th>\n",
       "      <th>state</th>\n",
       "      <th>post</th>\n",
       "      <th>treated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1929-07-01</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1930-07-01</td>\n",
       "      <td>1930</td>\n",
       "      <td>135</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1931-07-01</td>\n",
       "      <td>1931</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1932-07-01</td>\n",
       "      <td>1932</td>\n",
       "      <td>113</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1933-07-01</td>\n",
       "      <td>1933</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1934-07-01</td>\n",
       "      <td>1934</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1929-07-01</td>\n",
       "      <td>1929</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1930-07-01</td>\n",
       "      <td>1930</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1931-07-01</td>\n",
       "      <td>1931</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1932-07-01</td>\n",
       "      <td>1932</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1933-07-01</td>\n",
       "      <td>1933</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1934-07-01</td>\n",
       "      <td>1934</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  year  bib  state  post  treated\n",
       "0  1929-07-01  1929  141      1     0        0\n",
       "1  1930-07-01  1930  135      1     0        0\n",
       "2  1931-07-01  1931  121      1     1        1\n",
       "3  1932-07-01  1932  113      1     1        1\n",
       "4  1933-07-01  1933  102      1     1        1\n",
       "5  1934-07-01  1934  102      1     1        1\n",
       "6  1929-07-01  1929  169      0     0        0\n",
       "7  1930-07-01  1930  165      0     0        0\n",
       "8  1931-07-01  1931  132      0     1        0\n",
       "9  1932-07-01  1932  120      0     1        0\n",
       "10 1933-07-01  1933  111      0     1        0\n",
       "11 1934-07-01  1934  109      0     1        0"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "did_data = pd.concat(\n",
    "    [\n",
    "        data[['date', 'year', 'bib6']].rename(columns={\"bib6\": \"bib\"}).assign(state=1),\n",
    "        data[['date', 'year', 'bib8']].rename(columns={\"bib8\": \"bib\"}).assign(state=0),\n",
    "    ],\n",
    "    axis=0\n",
    ").reset_index(drop=True)\n",
    "\n",
    "did_data['post'] = (did_data['date'] > pd.to_datetime('1931-01-01')).astype(int)\n",
    "did_data['treated'] = did_data['post'] * did_data['state']\n",
    "\n",
    "did_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Causal Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to Equation(5.3) in the book, the reduced form of DID equation is as follows:\n",
    "\n",
    "$Y_{dt} = \\alpha + \\beta TREAT_{d} + \\gamma POST_{t} + \\delta_{rDID} (TREAT_{d} \\times POST_{t}) + \\epsilon_{dt}$,\n",
    "\n",
    "where:\n",
    "* $TREAT_{d}$ is the state-fixed effect, which is renamed \"state\" in the data cleaning process above\n",
    "* $POST_{t}$ is the time-fixed effect, renamed \"post\"\n",
    "* $TREAT_{d} \\times POST_{t}$ is the treatment variable, renamed \"treated\"\n",
    "\n",
    "Regression upon 12 data points, the estimated causal effect should be around 20.5 with the standard error arround 10.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `DoWhy` Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dowhy_did_model = CausalModel(\n",
    "    data=did_data,\n",
    "    treatment='treated',\n",
    "    outcome='bib',\n",
    "    common_causes=['post', 'state'],\n",
    ")\n",
    "\n",
    "dowhy_did_model.view_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'backdoor1': {'estimand': Derivative(Expectation(bib|post,state), [treated]),\n",
       "  'assumptions': {'Unconfoundedness': 'If U→{treated} and U→bib then P(bib|treated,post,state,U) = P(bib|treated,post,state)'}},\n",
       " 'backdoor2': {'estimand': Derivative(Expectation(bib|post,state), [treated]),\n",
       "  'assumptions': {'Unconfoundedness': 'If U→{treated} and U→bib then P(bib|treated,post,state,U) = P(bib|treated,post,state)'}},\n",
       " 'backdoor': {'estimand': Derivative(Expectation(bib|post,state), [treated]),\n",
       "  'assumptions': {'Unconfoundedness': 'If U→{treated} and U→bib then P(bib|treated,post,state,U) = P(bib|treated,post,state)'}},\n",
       " 'iv': None,\n",
       " 'frontdoor': None}"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "identified_estimand = dowhy_did_model.identify_effect()\n",
    "identified_estimand.estimands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Causal Estimate ***\n",
      "\n",
      "## Identified estimand\n",
      "Estimand type: EstimandType.NONPARAMETRIC_ATE\n",
      "\n",
      "### Estimand : 1\n",
      "Estimand name: backdoor\n",
      "Estimand expression:\n",
      "    d                        \n",
      "──────────(E[bib|post,state])\n",
      "d[treated]                   \n",
      "Estimand assumption 1, Unconfoundedness: If U→{treated} and U→bib then P(bib|treated,post,state,U) = P(bib|treated,post,state)\n",
      "\n",
      "## Realized estimand\n",
      "b: bib~treated+post+state\n",
      "Target units: ate\n",
      "\n",
      "## Estimate\n",
      "Mean value: 20.500000000000043\n",
      "p-value: [0.09222442]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/davidli/anaconda3/envs/mastering-py-metrics/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=12\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n",
      "/home/davidli/anaconda3/envs/mastering-py-metrics/lib/python3.11/site-packages/dowhy/causal_estimators/regression_estimator.py:179: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  intercept_parameter = self.model.params[0]\n"
     ]
    }
   ],
   "source": [
    "causal_estimate_reg = dowhy_did_model.estimate_effect(\n",
    "    identified_estimand,\n",
    "    method_name=\"backdoor.linear_regression\",\n",
    "    test_significance=True\n",
    ")\n",
    "\n",
    "print(causal_estimate_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `statsmodels` Implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to `R`'s formula api, in `statsmodels`, asterisk (*) represents generating the product of two dummy variables without removing them; colon (:) represents generating and leaving only the product but not the original variables. More information can be found in `statsmodels`' [documentation on R-styled formulas](https://www.statsmodels.org/devel/example_formulas.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                    bib   R-squared:                       0.891\n",
      "Model:                            OLS   Adj. R-squared:                  0.850\n",
      "Method:                 Least Squares   F-statistic:                     21.71\n",
      "Date:                Mon, 08 Jan 2024   Prob (F-statistic):           0.000337\n",
      "Time:                        14:15:51   Log-Likelihood:                -40.628\n",
      "No. Observations:                  12   AIC:                             89.26\n",
      "Df Residuals:                       8   BIC:                             91.20\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    167.0000      6.190     26.980      0.000     152.727     181.273\n",
      "post         -49.0000      7.581     -6.464      0.000     -66.481     -31.519\n",
      "state        -29.0000      8.754     -3.313      0.011     -49.186      -8.814\n",
      "post:state    20.5000     10.721      1.912      0.092      -4.222      45.222\n",
      "==============================================================================\n",
      "Omnibus:                        1.065   Durbin-Watson:                   1.668\n",
      "Prob(Omnibus):                  0.587   Jarque-Bera (JB):                0.856\n",
      "Skew:                           0.548   Prob(JB):                        0.652\n",
      "Kurtosis:                       2.285   Cond. No.                         8.44\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/davidli/anaconda3/envs/mastering-py-metrics/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=12\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "formula = 'bib ~ post*state'\n",
    "\n",
    "results = smf.ols(\n",
    "    formula=formula,\n",
    "    data=did_data\n",
    ").fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>bib6</th>\n",
       "      <th>bio6</th>\n",
       "      <th>bib8</th>\n",
       "      <th>bio8</th>\n",
       "      <th>lbib6</th>\n",
       "      <th>lbio6</th>\n",
       "      <th>lbib8</th>\n",
       "      <th>lbio8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1929-07-01</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>5.129899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1930-07-01</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1930</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>4.905275</td>\n",
       "      <td>4.905275</td>\n",
       "      <td>5.105945</td>\n",
       "      <td>5.105945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>1931-07-01</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1931</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>132</td>\n",
       "      <td>130</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>4.882802</td>\n",
       "      <td>4.867534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>1932-07-01</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1932</td>\n",
       "      <td>113</td>\n",
       "      <td>111</td>\n",
       "      <td>120</td>\n",
       "      <td>118</td>\n",
       "      <td>4.727388</td>\n",
       "      <td>4.709530</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>4.770685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>1933-07-01</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1933</td>\n",
       "      <td>102</td>\n",
       "      <td>100</td>\n",
       "      <td>111</td>\n",
       "      <td>110</td>\n",
       "      <td>4.624973</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>4.709530</td>\n",
       "      <td>4.700480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    weekday  day  month  year  bib6  bio6  bib8  bio8  \\\n",
       "0    1929-07-01     Monday    1      7  1929   141   141   169   169   \n",
       "365  1930-07-01    Tuesday    1      7  1930   135   135   165   165   \n",
       "730  1931-07-01  Wednesday    1      7  1931   121   121   132   130   \n",
       "1096 1932-07-01     Friday    1      7  1932   113   111   120   118   \n",
       "1461 1933-07-01   Saturday    1      7  1933   102   100   111   110   \n",
       "\n",
       "         lbib6     lbio6     lbib8     lbio8  \n",
       "0     4.948760  4.948760  5.129899  5.129899  \n",
       "365   4.905275  4.905275  5.105945  5.105945  \n",
       "730   4.795791  4.795791  4.882802  4.867534  \n",
       "1096  4.727388  4.709530  4.787492  4.770685  \n",
       "1461  4.624973  4.605170  4.709530  4.700480  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data = data.copy()\n",
    "plot_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>bib6</th>\n",
       "      <th>bio6</th>\n",
       "      <th>bib8</th>\n",
       "      <th>bio8</th>\n",
       "      <th>lbib6</th>\n",
       "      <th>lbio6</th>\n",
       "      <th>lbib8</th>\n",
       "      <th>lbio8</th>\n",
       "      <th>bib6_cf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1929-07-01</td>\n",
       "      <td>Monday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1929</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>169</td>\n",
       "      <td>169</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>4.948760</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>5.129899</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>1930-07-01</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1930</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>165</td>\n",
       "      <td>165</td>\n",
       "      <td>4.905275</td>\n",
       "      <td>4.905275</td>\n",
       "      <td>5.105945</td>\n",
       "      <td>5.105945</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>1931-07-01</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1931</td>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>132</td>\n",
       "      <td>130</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>4.795791</td>\n",
       "      <td>4.882802</td>\n",
       "      <td>4.867534</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1096</th>\n",
       "      <td>1932-07-01</td>\n",
       "      <td>Friday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1932</td>\n",
       "      <td>113</td>\n",
       "      <td>111</td>\n",
       "      <td>120</td>\n",
       "      <td>118</td>\n",
       "      <td>4.727388</td>\n",
       "      <td>4.709530</td>\n",
       "      <td>4.787492</td>\n",
       "      <td>4.770685</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>1933-07-01</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1933</td>\n",
       "      <td>102</td>\n",
       "      <td>100</td>\n",
       "      <td>111</td>\n",
       "      <td>110</td>\n",
       "      <td>4.624973</td>\n",
       "      <td>4.605170</td>\n",
       "      <td>4.709530</td>\n",
       "      <td>4.700480</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1826</th>\n",
       "      <td>1934-07-01</td>\n",
       "      <td>Sunday</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1934</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>109</td>\n",
       "      <td>108</td>\n",
       "      <td>4.624973</td>\n",
       "      <td>4.624973</td>\n",
       "      <td>4.691348</td>\n",
       "      <td>4.682131</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date    weekday  day  month  year  bib6  bio6  bib8  bio8  \\\n",
       "0    1929-07-01     Monday    1      7  1929   141   141   169   169   \n",
       "365  1930-07-01    Tuesday    1      7  1930   135   135   165   165   \n",
       "730  1931-07-01  Wednesday    1      7  1931   121   121   132   130   \n",
       "1096 1932-07-01     Friday    1      7  1932   113   111   120   118   \n",
       "1461 1933-07-01   Saturday    1      7  1933   102   100   111   110   \n",
       "1826 1934-07-01     Sunday    1      7  1934   102   102   109   108   \n",
       "\n",
       "         lbib6     lbio6     lbib8     lbio8  bib6_cf  \n",
       "0     4.948760  4.948760  5.129899  5.129899      138  \n",
       "365   4.905275  4.905275  5.105945  5.105945      135  \n",
       "730   4.795791  4.795791  4.882802  4.867534      108  \n",
       "1096  4.727388  4.709530  4.787492  4.770685       98  \n",
       "1461  4.624973  4.605170  4.709530  4.700480       90  \n",
       "1826  4.624973  4.624973  4.691348  4.682131       89  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate counterfactual benchmarking with 1930\n",
    "plot_data['bib6_cf'] = (\n",
    "    plot_data['bib8'] \n",
    "    / plot_data.set_index('year').loc[1930, 'bib8'] \n",
    "    * plot_data.set_index('year').loc[1930, 'bib6']\n",
    ").astype(int)\n",
    "\n",
    "plot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax: plt.Axes\n",
    "fig, ax = plt.subplots(figsize=(10,6), dpi=200)\n",
    "\n",
    "sns.lineplot(\n",
    "    data=plot_data,\n",
    "    x=\"year\",\n",
    "    y=\"bib6\",\n",
    "    marker=\"o\",\n",
    "    color='black',\n",
    "    label=\"Sixth District\"\n",
    ")\n",
    "\n",
    "sns.lineplot(\n",
    "    data=plot_data[plot_data['year'] >= 1930],\n",
    "    x=\"year\",\n",
    "    y=\"bib6_cf\",\n",
    "    marker=\"o\",\n",
    "    linestyle=\":\",\n",
    "    color='black',\n",
    "    label=\"Sixth District - Counterfactual\"\n",
    ")\n",
    "\n",
    "sns.lineplot(\n",
    "    data=plot_data,\n",
    "    x=\"year\",\n",
    "    y=\"bib8\",\n",
    "    marker=\"^\",\n",
    "    color='black',\n",
    "    label=\"Eighth District\"\n",
    ")\n",
    "\n",
    "ax.set_title(\"Trends in Bank Failures in the Sixth and Eighth Federal Reserve Districts\")\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylim([80, 180])\n",
    "ax.set_ylabel(\"Number of Banks in Business\")\n",
    "\n",
    "plt.legend()\n",
    "plt.savefig(\"../figs/banks.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mastering-py-metrics",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
